Demystify AI Decisions and Master Interpretability and Explainability Today

Book Description

Interpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust.

Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models.

You’ll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you’ll dive into post-hoc methods, feature effect analysis, anchors, and counterfactuals—powerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems.

Through hands-on Python examples, you’ll learn how to apply these techniques in real-world scenarios. By the end, you’ll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards—giving you a competitive edge in the evolving AI landscape.

Table of Contents

1. Interpreting Interpretable Machine Learning2. Model Types and Interpretability Techniques3. Interpretability Taxonomy and Techniques4. Feature Effects Analysis with Plots5. Post-Hoc Methods6. Anchors and Counterfactuals7. Interpretability in Neural Networks8. Explainable Neural Networks9. Explainability in Transformers and Large Language Models10. Explainability and Responsible AI
   
   Index